Computationally attractive nonlinear models for combinatorial optimisation
type: Item
instance of: scholarly article
title: Computationally attractive nonlinear models for combinatorial optimisation
zbMATH Open document ID: 1180.90257
DOI: 10.1504/IJMOR.2009.022873
author: Bahram Alidaee
author: Gary A. Kochenberger
author: Karen R. Lewis
author: Mark W. Lewis
author: Haibo Wang
published in: International Journal of Mathematics in Operational Research
publication date: 2009-10-12T00:00:00Z
review text: Summary: A common approach to many combinatorial problems is to model them as \(0/1\) linear programs. This approach enables the use of standard linear program-based optimisation methodologies that are widely employed by the operation research community. While this methodology has worked well for many problems, it can become problematic in cases where the linear programs generated become excessively large. In such cases, linear models can lose their computational viability. In recent years, several articles have explored the computational attractiveness of non-linear alternatives to the standard linear models typically adopted to represent such problems. In many cases, comparative computational testing yields results favouring the non-linear models by a wide margin. In this article, we summarise some of these successes in an effort to encourage a broader view of model construction than the conventional wisdom, i.e. linear modelling, typically affords.
Mathematics Subject Classification ID: 90C27
zbMATH DE Number: 5613382
zbMATH Keywords: integer programming
zbMATH Keywords: non-linear modelling
zbMATH Keywords: optimisation
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 5613382